def test_random():
# Sanity test to make sure that feature number positively impacts least squares error.
num_points = 100
num_data_per_point = 55
learning_rate = 0.5
x_in = np.random.normal(size=(num_data_per_point, num_points))
for num_features in [1, 5, 10, 15, 20, 40, 70]:
ae = AutoEncoder(x_in, num_features, random_seed=1234)
w_in = np.random.normal(size=(num_data_per_point, num_features))
z_out, least_squares_test = ae.psi(w_in)
print(
f"(# features : Least squares error = ({num_features} : {least_squares_test})"
)
print("Starting gradient decent...")
loss_values = [] # Keep track of loss values over epochs
for epoch in range(1000):
z_grd, ls_grd, grd = ae.calc_g(
w_in) # Calculate Z, Error, and Gradient Matrix
w_in = w_in - (learning_rate * grd
) # Update W using Gradient Matrix
loss_values.append(ls_grd) # Log loss
print(f"Epoch: {epoch}\t----------\tLoss: {ls_grd}")
# print(loss_values)
plotter.plot_loss(
loss_values,
f"Gradient Loss Over Epochs (test) (num_features: {num_features})")
